Generalisation of Alpha-Beta Search for AND-OR Graphs With Partially Ordered Values

Abstract

Chance constrained optimization problems allow to model problems where constraints involving stochastic components should only be violated with a small probability. Evolutionary algorithms have been applied to this scenario and shown to achieve high quality results. With this paper, we contribute to the theoretical understanding of evolutionary algorithms for chance constrained optimization. We study the scenario of stochastic components that are independent and Normally distributed. Considering the simple single-objective (1+1)~EA, we show that imposing an additional uniform constraint already leads to local optima for very restricted scenarios and an exponential optimization time. We therefore introduce a multi-objective formulation of the problem which trades off the expected cost and its variance. We show that multi-objective evolutionary algorithms are highly effective when using this formulation and obtain a set of solutions that contains an optimal solution for any possible confidence level imposed on the constraint. Furthermore, we prove that this approach can also be used to compute a set of optimal solutions for the chance constrained minimum spanning tree problem. Experimental investigations on instances of the NP-hard stochastic minimum weight dominating set problem confirm the benefit of the multi-objective approach in practice.

Opponent-Model Search in Games with Incomplete Information

“…This algorithm has several advantages: returned values for internal nodes are readily interpretable; the algorithm is efficient on memory since the recursion depth is the depth of the game tree, which in general is exponentially smaller than the tree; the search can be combined with other techniques, such as heuristic functions and αβ pruning (which is possible whenever (V, ∨, ∧) forms a lattice (Li et al 2022)), move ordering, Monte Carlo techniques such as MCTS, etc.…”